We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

NLP research is impeded by a lack of resources and awareness of the challenges presented
by underrepresented languages and dialects. Focusing on the languages spoken in …

Practice and recent evidence show that state-of-the-art (SotA) automatic speech recognition
(ASR) systems do not perform equally well for all speaker groups. Many factors can cause …

State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

As an essential task in task-oriented dialog systems, slot filling requires extensive training
data in a certain domain. However, such data are not always available. Hence, cross …

The variety of accents has posed a big challenge to speech recognition. The Accented
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the
proliferation of both fake and real information. Considering the problematic consequences …